"""simple docstring"""


import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


a				=logging.get_logger(__name__)

enable_full_determinism()






class   __UpperCAmelCase		(      __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase      ):
				A__ :  Union[str, Any]    				=			UNetaDModel
				A__ :  List[str]    				=			'''sample'''

				@property
				def 						_a      ( self ):
										lowerCamelCase__   =4
										lowerCamelCase__   =3
										lowerCamelCase__   =(32, 32)

										lowerCamelCase__   =floats_tensor((batch_size, num_channels) + sizes ).to(_lowerCamelCase )
										lowerCamelCase__   =torch.tensor([10] ).to(_lowerCamelCase )

										return {"sample": noise, "timestep": time_step}

				@property
				def 						_a      ( self ):
										return (3, 32, 32)

				@property
				def 						_a      ( self ):
										return (3, 32, 32)






				def 						_a      ( self ):
										lowerCamelCase__   ={
										    "block_out_channels": (32, 64),
										    "down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
										    "up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
										    "attention_head_dim": 3,
										    "out_channels": 3,
										    "in_channels": 3,
										    "layers_per_block": 2,
										    "sample_size": 32,
										}
										lowerCamelCase__   =self.dummy_input
										return init_dict, inputs_dict






class   __UpperCAmelCase		(      __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase      ):
				A__ :  List[Any]    				=			UNetaDModel
				A__ :  Tuple    				=			'''sample'''

				@property
				def 						_a      ( self ):
										lowerCamelCase__   =4
										lowerCamelCase__   =4
										lowerCamelCase__   =(32, 32)

										lowerCamelCase__   =floats_tensor((batch_size, num_channels) + sizes ).to(_lowerCamelCase )
										lowerCamelCase__   =torch.tensor([10] ).to(_lowerCamelCase )

										return {"sample": noise, "timestep": time_step}

				@property
				def 						_a      ( self ):
										return (4, 32, 32)

				@property
				def 						_a      ( self ):
										return (4, 32, 32)

				def 						_a      ( self ):
										lowerCamelCase__   ={
										    "sample_size": 32,
										    "in_channels": 4,
										    "out_channels": 4,
										    "layers_per_block": 2,
										    "block_out_channels": (32, 64),
										    "attention_head_dim": 32,
										    "down_block_types": ("DownBlock2D", "DownBlock2D"),
										    "up_block_types": ("UpBlock2D", "UpBlock2D"),
										}
										lowerCamelCase__   =self.dummy_input
										return init_dict, inputs_dict

				def 						_a      ( self ):
										lowerCamelCase__   , lowerCamelCase__   =UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"      ,					output_loading_info=_lowerCamelCase )

										self.assertIsNotNone(_lowerCamelCase )
										self.assertEqual(len(loading_info["missing_keys"] )      ,					0 )

										model.to(_lowerCamelCase )
										lowerCamelCase__   =model(**self.dummy_input ).sample

										assert image is not None, "Make sure output is not None"

				@unittest.skipIf(torch_device != "cuda"      ,					"This test is supposed to run on GPU" )
				def 						_a      ( self ):
										lowerCamelCase__   , lowerCamelCase__   =UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"      ,					output_loading_info=_lowerCamelCase )
										model.to(_lowerCamelCase )
										lowerCamelCase__   =model(**self.dummy_input ).sample

										assert image is not None, "Make sure output is not None"

				@unittest.skipIf(torch_device != "cuda"      ,					"This test is supposed to run on GPU" )
				def 						_a      ( self ):
										# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
										lowerCamelCase__   , lowerCamelCase__   =UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"      ,					output_loading_info=_lowerCamelCase )
										model_accelerate.to(_lowerCamelCase )
										model_accelerate.eval()

										lowerCamelCase__   =torch.randn(
										    1      ,					model_accelerate.config.in_channels      ,					model_accelerate.config.sample_size      ,					model_accelerate.config.sample_size      ,					generator=torch.manual_seed(0 )      ,					)
										lowerCamelCase__   =noise.to(_lowerCamelCase )
										lowerCamelCase__   =torch.tensor([10] * noise.shape[0] ).to(_lowerCamelCase )

										lowerCamelCase__   =model_accelerate(_lowerCamelCase      ,					_lowerCamelCase )["sample"]

										# two models don't need to stay in the device at the same time
										del model_accelerate
										torch.cuda.empty_cache()
										gc.collect()

										lowerCamelCase__   , lowerCamelCase__   =UNetaDModel.from_pretrained(
										    "fusing/unet-ldm-dummy-update"      ,					output_loading_info=_lowerCamelCase      ,					low_cpu_mem_usage=_lowerCamelCase )
										model_normal_load.to(_lowerCamelCase )
										model_normal_load.eval()
										lowerCamelCase__   =model_normal_load(_lowerCamelCase      ,					_lowerCamelCase )["sample"]

										assert torch_all_close(_lowerCamelCase      ,					_lowerCamelCase      ,					rtol=1E-3 )






				def 						_a      ( self ):
										lowerCamelCase__   =UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" )
										model.eval()
										model.to(_lowerCamelCase )

										lowerCamelCase__   =torch.randn(
										    1      ,					model.config.in_channels      ,					model.config.sample_size      ,					model.config.sample_size      ,					generator=torch.manual_seed(0 )      ,					)
										lowerCamelCase__   =noise.to(_lowerCamelCase )
										lowerCamelCase__   =torch.tensor([10] * noise.shape[0] ).to(_lowerCamelCase )

										with torch.no_grad():
																lowerCamelCase__   =model(_lowerCamelCase      ,					_lowerCamelCase ).sample

										lowerCamelCase__   =output[0, -1, -3:, -3:].flatten().cpu()
										# fmt: off
										lowerCamelCase__   =torch.tensor([-1_3.3_2_5_8, -2_0.1_1_0_0, -1_5.9_8_7_3, -1_7.6_6_1_7, -2_3.0_5_9_6, -1_7.9_4_1_9, -1_3.3_6_7_5, -1_6.1_8_8_9, -1_2.3_8_0_0] )
										# fmt: on

										self.assertTrue(torch_all_close(_lowerCamelCase      ,					_lowerCamelCase      ,					rtol=1E-3 ) )






class   __UpperCAmelCase		(      __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase      ):
				A__ :  List[Any]    				=			UNetaDModel
				A__ :  Optional[Any]    				=			'''sample'''

				@property
				def 						_a      ( self      ,					_lowerCamelCase=(32, 32) ):
										lowerCamelCase__   =4
										lowerCamelCase__   =3

										lowerCamelCase__   =floats_tensor((batch_size, num_channels) + sizes ).to(_lowerCamelCase )
										lowerCamelCase__   =torch.tensor(batch_size * [10] ).to(dtype=torch.intaa      ,					device=_lowerCamelCase )

										return {"sample": noise, "timestep": time_step}

				@property
				def 						_a      ( self ):
										return (3, 32, 32)

				@property
				def 						_a      ( self ):
										return (3, 32, 32)

				def 						_a      ( self ):
										lowerCamelCase__   ={
										    "block_out_channels": [32, 64, 64, 64],
										    "in_channels": 3,
										    "layers_per_block": 1,
										    "out_channels": 3,
										    "time_embedding_type": "fourier",
										    "norm_eps": 1E-6,
										    "mid_block_scale_factor": math.sqrt(2.0 ),
										    "norm_num_groups": None,
										    "down_block_types": [
										        "SkipDownBlock2D",
										        "AttnSkipDownBlock2D",
										        "SkipDownBlock2D",
										        "SkipDownBlock2D",
										    ],
										    "up_block_types": [
										        "SkipUpBlock2D",
										        "SkipUpBlock2D",
										        "AttnSkipUpBlock2D",
										        "SkipUpBlock2D",
										    ],
										}
										lowerCamelCase__   =self.dummy_input
										return init_dict, inputs_dict

				@slow
				def 						_a      ( self ):
										lowerCamelCase__   , lowerCamelCase__   =UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"      ,					output_loading_info=_lowerCamelCase )
										self.assertIsNotNone(_lowerCamelCase )
										self.assertEqual(len(loading_info["missing_keys"] )      ,					0 )

										model.to(_lowerCamelCase )
										lowerCamelCase__   =self.dummy_input
										lowerCamelCase__   =floats_tensor((4, 3) + (256, 256) ).to(_lowerCamelCase )
										lowerCamelCase__   =noise
										lowerCamelCase__   =model(**_lowerCamelCase )

										assert image is not None, "Make sure output is not None"

				@slow
				def 						_a      ( self ):
										lowerCamelCase__   =UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256" )
										model.to(_lowerCamelCase )

										lowerCamelCase__   =4
										lowerCamelCase__   =3
										lowerCamelCase__   =(256, 256)

										lowerCamelCase__   =torch.ones((batch_size, num_channels) + sizes ).to(_lowerCamelCase )
										lowerCamelCase__   =torch.tensor(batch_size * [1E-4] ).to(_lowerCamelCase )

										with torch.no_grad():
																lowerCamelCase__   =model(_lowerCamelCase      ,					_lowerCamelCase ).sample

										lowerCamelCase__   =output[0, -3:, -3:, -1].flatten().cpu()
										# fmt: off
										lowerCamelCase__   =torch.tensor([-4_8_4_2.8_6_9_1, -6_4_9_9.6_6_3_1, -3_8_0_0.1_9_5_3, -7_9_7_8.2_6_8_6, -1_0_9_8_0.7_1_2_9, -2_0_0_2_8.8_5_3_5, 8_1_4_8.2_8_2_2, 2_3_4_2.2_9_0_5, 5_6_7.7_6_0_8] )
										# fmt: on

										self.assertTrue(torch_all_close(_lowerCamelCase      ,					_lowerCamelCase      ,					rtol=1E-2 ) )

				def 						_a      ( self ):
										lowerCamelCase__   =UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update" )
										model.to(_lowerCamelCase )

										lowerCamelCase__   =4
										lowerCamelCase__   =3
										lowerCamelCase__   =(32, 32)

										lowerCamelCase__   =torch.ones((batch_size, num_channels) + sizes ).to(_lowerCamelCase )
										lowerCamelCase__   =torch.tensor(batch_size * [1E-4] ).to(_lowerCamelCase )

										with torch.no_grad():
																lowerCamelCase__   =model(_lowerCamelCase      ,					_lowerCamelCase ).sample

										lowerCamelCase__   =output[0, -3:, -3:, -1].flatten().cpu()
										# fmt: off
										lowerCamelCase__   =torch.tensor([-0.0_3_2_5, -0.0_9_0_0, -0.0_8_6_9, -0.0_3_3_2, -0.0_7_2_5, -0.0_2_7_0, -0.0_1_0_1, 0.0_2_2_7, 0.0_2_5_6] )
										# fmt: on

										self.assertTrue(torch_all_close(_lowerCamelCase      ,					_lowerCamelCase      ,					rtol=1E-2 ) )






				def 						_a      ( self ):
										# not required for this model
										pass



"""simple docstring"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_albert import AlbertTokenizer
else:
	a				=None

a				=logging.get_logger(__name__)
a				={'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

a				={
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

a				={
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

a				='▁'






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  Dict    				=			VOCAB_FILES_NAMES
				A__ :  List[Any]    				=			PRETRAINED_VOCAB_FILES_MAP
				A__ :  List[Any]    				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A__ :  Union[str, Any]    				=			AlbertTokenizer

				def __init__( self      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=True      ,					_lowerCamelCase=True      ,					_lowerCamelCase=False      ,					_lowerCamelCase="[CLS]"      ,					_lowerCamelCase="[SEP]"      ,					_lowerCamelCase="<unk>"      ,					_lowerCamelCase="[SEP]"      ,					_lowerCamelCase="<pad>"      ,					_lowerCamelCase="[CLS]"      ,					_lowerCamelCase="[MASK]"      ,					**_lowerCamelCase      ,					):
										# Mask token behave like a normal word, i.e. include the space before it and
										# is included in the raw text, there should be a match in a non-normalized sentence.
										lowerCamelCase__   =(
										    AddedToken(_lowerCamelCase      ,					lstrip=_lowerCamelCase      ,					rstrip=_lowerCamelCase      ,					normalized=_lowerCamelCase )
										    if isinstance(_lowerCamelCase      ,					_lowerCamelCase )
										    else mask_token
										)

										super().__init__(
										    _lowerCamelCase      ,					tokenizer_file=_lowerCamelCase      ,					do_lower_case=_lowerCamelCase      ,					remove_space=_lowerCamelCase      ,					keep_accents=_lowerCamelCase      ,					bos_token=_lowerCamelCase      ,					eos_token=_lowerCamelCase      ,					unk_token=_lowerCamelCase      ,					sep_token=_lowerCamelCase      ,					pad_token=_lowerCamelCase      ,					cls_token=_lowerCamelCase      ,					mask_token=_lowerCamelCase      ,					**_lowerCamelCase      ,					)

										lowerCamelCase__   =do_lower_case
										lowerCamelCase__   =remove_space
										lowerCamelCase__   =keep_accents
										lowerCamelCase__   =vocab_file
										lowerCamelCase__   =False if not self.vocab_file else True

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase = None ):
										lowerCamelCase__   =[self.sep_token_id]
										lowerCamelCase__   =[self.cls_token_id]
										if token_ids_a is None:
																return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase = None ):
										lowerCamelCase__   =[self.sep_token_id]
										lowerCamelCase__   =[self.cls_token_id]

										if token_ids_a is None:
																return len(cls + token_ids_a + sep ) * [0]
										return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]






				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase = None ):
										if not self.can_save_slow_tokenizer:
																raise ValueError(
																    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																    "tokenizer." )

										if not os.path.isdir(_lowerCamelCase ):
																logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
																return
										lowerCamelCase__   =os.path.join(
										    _lowerCamelCase      ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

										if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ):
																copyfile(self.vocab_file      ,					_lowerCamelCase )

										return (out_vocab_file,)


